Influence of age and gender on the profile of exhaled volatile organic compounds analyzed by an electronic nose

Exhaled breath analysis for the discrimination of asthma and chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) and asthma are the most common chronic respiratory diseases. In middle-aged and elderly patients, it is difficult to distinguish between COPD and asthma based on clinical symptoms and pulmonary function examinations in clinical practice. Thus, an accurate and reliable inspection method is required. In this study, we aimed to identify breath biomarkers and evaluate the accuracy of breathomics-based methods for discriminating between COPD and asthma. In this multi-center cross-sectional study, exhaled breath samples were collected from 89 patients with COPD and 73 with asthma and detected on a high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) platform from 20 October 2022, to 20 May 2023, in four hospitals. Data analysis was performed from 15 June 2023 to 16 August 2023. The sensitivity, specificity, and accuracy were calculated to assess the overall performance of the volatile organic component (VOC)-based COPD and asthma discrimination models. Potential VOC markers related to COPD and asthma were also analyzed. The age of all participants ranged from to 18-86 years, and 54 (33.3%) were men. The age [median (minimum, maximum)] of COPD and asthma participants were 66.0 (46.0, 86.0), and 44.0 (17.0, 80.0). The male and female ratio of COPD and asthma participants were 14/75 and 40/33, respectively. Based on breathomics feature selection, ten VOCs were identified as COPD and asthma discrimination biomarkers via breath testing. The joint panel of these ten VOCs achieved an area under the curve of 0.843, sensitivity of 75.9%, specificity of 87.5%, and accuracy of 80.0% in COPD and asthma discrimination. Furthermore, the VOCs detected in the breath samples were closely related to the clinical characteristics of COPD and asthma. The VOC-based COPD and asthma discrimination model showed good accuracy, providing a new strategy for clinical diagnosis. Breathomics-based methods may play an important role in the diagnosis of COPD and asthma.

Disease diagnosis and severity classification in pulmonary fibrosis using carbonyl volatile organic compounds in exhaled breath

BACKGROUND

Pathophysiological conditions underlying pulmonary fibrosis remain poorly understood. Exhaled breath volatile organic compounds (VOCs) have shown promise for lung disease diagnosis and classification. In particular, carbonyls are a byproduct of oxidative stress, associated with fibrosis in the lungs. To explore the potential of exhaled carbonyl VOCs to reflect underlying pathophysiological conditions in pulmonary fibrosis, this proof-of-concept study tested the hypothesis that volatile and low abundance carbonyl compounds could be linked to diagnosis and associated disease severity.

METHODS

Exhaled breath samples were collected from outpatients with a diagnosis of Idiopathic Pulmonary Fibrosis (IPF) or Connective Tissue related Interstitial Lung Disease (CTD-ILD) with stable lung function for 3 months before enrollment, as measured by pulmonary function testing (PFT) DLCO (%), FVC (%) and FEV1 (%). A novel microreactor was used to capture carbonyl compounds in the breath as direct output products. A machine learning workflow was implemented with the captured carbonyl compounds as input features for classification of diagnosis and disease severity based on PFT (DLCO and FVC normal/mild vs. moderate/severe; FEV1 normal/mild/moderate vs. moderately severe/severe).

RESULTS

The proposed approach classified diagnosis with AUROC=0.877 ± 0.047 in the validation subsets. The AUROC was 0.820 ± 0.064, 0.898 ± 0.040, and 0.873 ± 0.051 for disease severity based on DLCO, FEV1, and FVC measurements, respectively. Eleven key carbonyl VOCs were identified with the potential to differentiate diagnosis and to classify severity.

CONCLUSIONS

Exhaled breath carbonyl compounds can be linked to pulmonary function and fibrotic ILD diagnosis, moving towards improved pathophysiological understanding of pulmonary fibrosis.

Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data

BACKGROUND

Breath testing using an electronic nose has been recognized as a promising new technique for the early detection of lung cancer. Imbalanced data are commonly observed in electronic nose studies, but methods to address them are rarely reported.

OBJECTIVE

The objectives of this study were to assess the accuracy of electronic nose screening for lung cancer with imbalanced learning and to select the best mechanical learning algorithm.

METHODS

We conducted a case‒control study that included patients with lung cancer and healthy controls and analyzed metabolites in exhaled breath using a carbon nanotube sensor array. The study used five machine learning algorithms to build predictive models and a synthetic minority oversampling technique to address imbalanced data. The diagnostic accuracy of lung cancer was assessed using pathology reports as the gold standard.

RESULTS

We enrolled 190 subjects between 2020 and 2023. A total of 155 subjects were used in the final analysis, which included 111 lung cancer patients and 44 healthy controls. We randomly divided samples into one training set, one internal validation set, and one external validation set. In the external validation set, the summary sensitivity was 0.88 (95% CI 0.84-0.91), the summary specificity was 1.00 (95% CI 0.85-1.00), the AUC was 0.96 (95% CI 0.94-0.98), the pAUC was 0.92 (95% CI 0.89-0.96), and the DOR was 207.62 (95% CI 24.62-924.64).

CONCLUSION

Electronic nose screening for lung cancer is highly accurate. The support vector machine algorithm is more suitable for analyzing chemical sensor data from electronic noses.

Towards detection of cancer biomarkers in human exhaled air by transfer-learning-powered analysis of odor-evoked calcium activity in rat olfactory bulb

Detection of volatile organic compounds in exhaled air is a promising approach to non-invasive and scalable gastric cancer screening. This work proposes a new approach for the detection of volatile organic compounds by analyzing odor-evoked calcium responses in the rat olfactory bulb. We estimate the feasibility of gastric cancer biomarker detection added to the exhaled air of healthy participants. Our detector consists of a convolutional encoder and a similarity-based classifier over encoder outputs. To minimize overfitting on a small available training set, we involve a pre-training where the encoder is trained on synthetic data representing spatiotemporal patterns similar to real calcium responses in the olfactory bulb. We estimate the classification accuracy of exhaled air samples by matching their encodings with encodings of calibration samples of two classes: 1) exhaled air and 2) a mixture of exhaled air with the cancer biomarker. On our data, the accuracy increased from 0.68 on real data up to 0.74 if pre-training on synthetic data is involved. Our work is focused on proving the feasibility of proposed new approach rather than on comparing its efficiency with existing methods. Such detection is often performed with an electronic nose, but its output becomes unstable over time due to a sensor drift. In contrast to the electronic nose, rats can robustly detect low concentrations of biomarkers over lifetime. The feasibility of gastric cancer biomarker detection in exhaled air by bio-hybrid system is shown. Pre-training of neural models for images analysis increases the accuracy of detection.

Breath Prints for Diagnosing Asthma in Children

Electronic nose (e-nose) is a new technology applied for the identification of volatile organic compounds (VOC) in breath air. Measuring VOC in exhaled breath can adequately identify airway inflammation, especially in asthma. Its noninvasive character makes e-nose an attractive technology applicable in pediatrics. We hypothesized that an electronic nose could discriminate the breath prints of patients with asthma from controls. A cross-sectional study was conducted and included 35 pediatric patients. Eleven cases and seven controls formed the two training models (models A and B). Another nine cases and eight controls formed the external validation group. Exhaled breath samples were analyzed using Cyranose 320, Smith Detections, Pasadena, CA, USA. The discriminative ability of breath prints was investigated by principal component analysis (PCA) and canonical discriminative analysis (CDA). Cross-validation accuracy (CVA) was calculated. For the external validation step, accuracy, sensitivity and specificity were calculated. Duplicate sampling of exhaled breath was obtained for ten patients. E-nose was able to discriminate between the controls and asthmatic patient group with a CVA of 63.63% and an M-distance of 3.13 for model A and a CVA of 90% and an M-distance of 5.55 for model B in the internal validation step. In the second step of external validation, accuracy, sensitivity and specificity were 64%, 77% and 50%, respectively, for model A, and 58%, 66% and 50%, respectively, for model B. Between paired breath sample fingerprints, there were no significant differences. An electronic nose can discriminate pediatric patients with asthma from controls, but the accuracy obtained in the external validation was lower than the CVA obtained in the internal validation step.

The Role of a Polymer-Based E-Nose in the Detection of Head and Neck Cancer from Exhaled Breath

The aim of our study was to assess whether a polymer-based e-nose can distinguish head and neck cancer subjects from healthy controls, as well as from patients with allergic rhinitis. A total number of 45 subjects participated in this study. The first group was composed of 15 patients with histology confirmed diagnosis of head and neck cancer. The second group was made up of 15 patients with diagnoses of allergic rhinitis. The control group consisted of 15 subjects with a negative history of upper airways and/or chest symptoms. Exhaled breath was collected from all participants and sampled by a polymer-based e-nose (Cyranose 320, Sensigent, Pasadena, CA, USA). In the Principal Component Analysis plot, patients with head and neck cancer clustered distinctly from the controls as well as from patients with allergic rhinitis. Using canonical discriminant analysis, the three groups were discriminated, with a cross validated accuracy% of 75.1, p < 0.01. The area under the curve of the receiver operating characteristic curve for the discrimination between head and neck cancer patients and the other groups was 0.87. To conclude, e-nose technology has the potential for application in the diagnosis of head and neck cancer, being an easy, quick, non-invasive and cost-effective tool.

Preliminary method for profiling volatile organic compounds in breath that correlate with pulmonary function and other clinical traits of subjects diagnosed with cystic fibrosis: a pilot study

Cystic fibrosis (CF) is characterized by chronic respiratory infections which progressively decrease lung function over time. Affected individuals experience episodes of intensified respiratory symptoms called pulmonary exacerbations (PEx) which accelerate pulmonary function decline and decrease survival. There is no standard classification for PEx, which results in treatments that are heterogeneous. Improving PEx classification and management is a significant priority for people with CF. Previous studies have shown volatile organic compounds (VOCs) in exhaled breath can be used as biomarkers because they are products of metabolic pathways dysregulated by different diseases. To provide insights on PEx classification and other clinical factors, exhaled breath was collected from subjects with CF, with some experiencing PEx and others at baseline. Exhaled breath was collected in Tedlar bags during tidal breathing for VOC analysis by solid phase microextraction coupled to gas chromatography-mass spectrometry. Statistical significance testing between quantitative and categorical clinical variables displayed percent-predicted forced expiratory volume in one second (FEV1pp) was decreased in subjects experiencing PEx. VOCs correlating with other clinical variables (body mass index, age, use of highly effective modulator therapies, and need for antibiotics) were also explored. VOCs correlating to potential confounding variables were removed and analyzed by regression for correlations with FEV1pp measurements. The VOC with the highest correlation with FEV1pp (3,7-dimethyldecane) also gave the lowest p-value when comparing subjects at baseline and during PEx. Receiver operator characteristic curves showed 3,7-dimethyldecane had a higher ability to classify PEx (area under the curve (AUC) = 0.91) relative to FEV1pp values at collection (AUC = 0.83). However, normalized ΔFEV1pp values had the highest capability to distinguish PEx (AUC = 0.93). These results show that exhaled VOCs may be a source of biomarkers for various clinical traits of CF, including PEx, that should be explored in larger sample cohorts and validation studies.

Differentiation of aspirated nasal air from room air using analysis with a differential mobility spectrometry-based electronic nose: a proof-of-concept study

Over the last few decades, breath analysis using electronic nose (eNose) technology has become a topic of intense research, as it is both non-invasive and painless, and is suitable for point-of-care use. To date, however, only a few studies have examined nasal air. As the air in the oral cavity and the lungs differs from the air in the nasal cavity, it is unknown whether aspirated nasal air could be exploited with eNose technology. Compared to traditional eNoses, differential mobility spectrometry uses an alternating electrical field to discriminate the different molecules of gas mixtures, providing analogous information. This study reports the collection of nasal air by aspiration and the subsequent analysis of the collected air using a differential mobility spectrometer. We collected nasal air from ten volunteers into breath collecting bags and compared them to bags of room air and the air aspirated through the device. Distance and dissimilarity metrics between the sample types were calculated and statistical significance evaluated with Kolmogorov-Smirnov test. After leave-one-day-out cross-validation, a shrinkage linear discriminant classifier was able to correctly classify 100% of the samples. The nasal air differed (p< 0.05) from the other sample types. The results show the feasibility of collecting nasal air by aspiration and subsequent analysis using differential mobility spectrometry, and thus increases the potential of the method to be used in disease detection studies.

The investigation of the volatile metabolites of lung cancer from the microenvironment of malignant pleural effusion

For malignant pleural effusions, pleural fluid cytology is a diagnostic method, but sensitivity is low. The pleural fluid contains metabolites directly released from cancer cells. The objective of this study was to diagnose lung cancer with malignant pleural effusion using the volatilomic profiling method. We recruited lung cancer patients with malignant pleural effusion and patients with nonmalignant diseases with pleural effusion as controls. We analyzed the headspace air of the pleural effusion by gas chromatography-mass spectrometry. We used partial least squares discriminant analysis (PLS-DA) to identify metabolites and the support vector machine (SVM) to establish the prediction model. We split data into a training set (80%) and a testing set (20%) to validate the accuracy. A total of 68 subjects were included in the final analysis. The PLS-DA showed high discrimination with an R² of 0.95 and Q² of 0.58. The accuracy of the SVM in the test set was 0.93 (95% CI 0.66, 0.998), the sensitivity was 83%, the specificity was 100%, and kappa was 0.85, and the area under the receiver operating characteristic curve was 0.96 (95% CI 0.86, 1.00). Volatile metabolites of pleural effusion might be used in patients with cytology-negative pleural effusion to rule out malignancy.

Breathing Rhythm Variations during Wash-In Do Not Influence Exhaled Volatile Organic Compound Profile Analyzed by an Electronic Nose

E-noses are innovative tools used for exhaled volatile organic compound (VOC) analysis, which have shown their potential in several diseases. Before obtaining a full validation of these instruments in clinical settings, a number of methodological issues still have to be established. We aimed to assess whether variations in breathing rhythm during wash-in with VOC-filtered air before exhaled air collection reflect changes in the exhaled VOC profile when analyzed by an e-nose (Cyranose 320). We enrolled 20 normal subjects and randomly collected their exhaled breath at three different breathing rhythms during wash-in: (a) normal rhythm (respiratory rate (RR) between 12 and 18/min), (b) fast rhythm (RR > 25/min) and (c) slow rhythm (RR < 10/min). Exhaled breath was collected by a previously validated method (Dragonieri et al., J. Bras. Pneumol. 2016) and analyzed by the e-nose. Using principal component analysis (PCA), no significant variations in the exhaled VOC profile were shown among the three breathing rhythms. Subsequent linear discriminant analysis (LDA) confirmed the above findings, with a cross-validated accuracy of 45% (p = ns). We concluded that the exhaled VOC profile, analyzed by an e-nose, is not influenced by variations in breathing rhythm during wash-in.

Exhaled breath analysis by use of eNose technology: a novel diagnostic tool for interstitial lung disease

INTRODUCTION

Early and accurate diagnosis of interstitial lung diseases (ILDs) remains a major challenge. Better noninvasive diagnostic tools are much needed. We aimed to assess the accuracy of exhaled breath analysis using eNose technology to discriminate between ILD patients and healthy controls, and to distinguish ILD subgroups.

METHODS

In this cross-sectional study, exhaled breath of consecutive ILD patients and healthy controls was analysed using eNose technology (SpiroNose). Statistical analyses were done using partial least square discriminant analysis and receiver operating characteristic analysis. Independent training and validation sets (2:1) were used in larger subgroups.

RESULTS

A total of 322 ILD patients and 48 healthy controls were included: sarcoidosis (n=141), idiopathic pulmonary fibrosis (IPF) (n=85), connective tissue disease-associated ILD (n=33), chronic hypersensitivity pneumonitis (n=25), idiopathic nonspecific interstitial pneumonia (n=10), interstitial pneumonia with autoimmune features (n=11) and other ILDs (n=17). eNose sensors discriminated between ILD and healthy controls, with an area under the curve (AUC) of 1.00 in the training and validation sets. Comparison of patients with IPF and patients with other ILDs yielded an AUC of 0.91 (95% CI 0.85-0.96) in the training set and an AUC of 0.87 (95% CI 0.77-0.96) in the validation set. The eNose reliably distinguished between individual diseases, with AUC values ranging from 0.85 to 0.99.

CONCLUSIONS

eNose technology can completely distinguish ILD patients from healthy controls and can accurately discriminate between different ILD subgroups. Hence, exhaled breath analysis using eNose technology could be a novel biomarker in ILD, enabling timely diagnosis in the future.

Detecting Multiple Sclerosis via breath analysis using an eNose, a pilot study

OBJECTIVE

In the present study we investigated whether Multiple Sclerosis (MS) can be detected via exhaled breath analysis using an electronic nose. The AeonoseTM (an electronic nose, The eNose Company, Zutphen, The Netherlands) is a diagnostic test device to detect patterns of volatile organic compounds in exhaled breath. We evaluated whether the AeonoseTM can make a distinction between the breath patterns of patients with MS and healthy control subjects.

METHODS

In this mono-center, prospective, non-invasive study, 124 subjects with a confirmed diagnosis of MS and 129 control subjects each breathed into the AeonoseTM for 5 minutes. Exhaled breath data was used to train an artificial neural network (ANN) predictive model. To investigate the influence of medication intake we created a second predictive model with a subgroup of MS patients without medication prescribed for MS.

RESULTS

The ANN model based on the entire dataset was able to distinguish MS patients from healthy controls with a sensitivity of 0.75 [95% CI: 0.66-0.82] and specificity of 0.60 [0.51-0.69]. The model created with the subgroup of MS patients not using medication and the healthy control subjects had a sensitivity of 0.93 [0.82-0.98] and a specificity of 0.74 [0.65-0.81].

CONCLUSION

The study showed that the AeonoseTM is able to make a distinction between MS patients and healthy control subjects, and could potentially provide a quick screening test to assist in diagnosing MS. Further research is needed to determine whether the AeonoseTM is able to differentiate new MS patients from subjects who will not get the diagnosis.

Volatile organic compounds in a headspace sampling system and asthmatics sputum samples

Background:The headspace of a biological sample contains exogenous VOCs present within the sampling environment which represent the background signal.Study aims:This study aimed to characterise the background signal generated from a headspace sampling system in a clinical site, to evaluate intra- and inter-day variation of background VOC and to understand the impact of a sample itself upon commonly reported background VOC using sputum headspace samples from severe asthmatics.Methods:The headspace, in absence of a biological sample, was collected hourly from 11am to 3pm within a day (time of clinical samples acquisition), and from Monday to Friday in a week, and analysed by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). Chemometric analysis identified 1120 features, 37 of which were present in at least the 80% of all the samples. The analyses of intra- and inter-day background variations were performed on thirteen of the most abundant features, ubiquitously present in headspace samples. The concentration ratios relative to background were reported for the selected abundant VOC in 36 asthmatic sputum samples, acquired from 36 stable severe asthma patients recruited at Glenfield Hospital, Leicester, UK.Results:The results identified no significant intra- or inter-day variations in compounds levels and no systematic bias of z-scores, with the exclusion of benzothiazole, whose abundance increased linearly between 11am and 3pm with a maximal intra-day fold change of 2.13. Many of the identified background features are reported in literature as components of headspace of biological samples and are considered potential biomarkers for several diseases. The selected background features were identified in headspace of all severe asthma sputum samples, albeit with varying levels of enrichment relative to background.Conclusion:Our observations support the need to consider the background signal derived from the headspace sampling system when developing and validating headspace biomarker signatures using clinical samples.

Breath analysis in detecting epilepsy

The aim of this proof of concept study is to investigate if an electronic nose (eNose) is able to make a distinction between breath profiles of diagnosed epilepsy patients and epilepsy-free control subjects. An eNose is a non-invasive device, with a working mechanism that is based on the presence of volatile organic compounds (VOCs) in exhaled breath. These VOCs interact with the sensors of the eNose, and the eNose has to be trained to distinguish between breath patterns from patients with a specific disease and control subjects without that disease. During the measurement participants were asked to breathe through the eNose for five minutes via a disposable mouthpiece. Seventy-four epilepsy patients and 110 control subjects were measured to train the eNose and create a classification model. To assess the effects of anti-epileptic drugs (AEDs) usage on the classification, additional test groups were measured: seven patients who (temporarily) did not use AEDs and 11 patients without epilepsy who used AEDs. The results show that an eNose is able to make a distinction between epilepsy and control subjects with a sensitivity of 76%, a specificity of 67%, and an accuracy of 71%. The results of the two additional groups of subjects show that the created model classifies one out of seven epilepsy patients without AEDs and six out of 13 patients without epilepsy but with AEDs correctly. In this proof of concept study, the Aeonose^TM is able to differentiate between epilepsy patients and control subjects. However, the number of false positives and false negatives is still high, which suggests that this first model is still mainly based on the usage of various AEDs.

Exhaled Breath Analysis in Diagnosis of Malignant Pleural Mesothelioma: Systematic Review

Malignant pleural mesothelioma (MPM) is mainly related to previous asbestos exposure. There is still dearth of information on non-invasive biomarkers to detect MPM at early stages. Human studies on exhaled breath biomarkers of cancer and asbestos-related diseases show encouraging results. The aim of this systematic review was to provide an overview on the current knowledge about exhaled breath analysis in MPM diagnosis. A systematic review was conducted on MEDLINE (PubMed), EMBASE and Web of Science databases to identify relevant studies. Quality assessment was done by the Newcastle-Ottawa Scale. Six studies were identified, all of which showed fair quality and explored volatile organic compounds (VOC) based breath profile using Gas Chromatography Coupled to Mass Spectrometry (GC-MS), Ion Mobility Spectrometry Coupled to Multi-capillary Columns (IMS-MCC) or pattern-recognition technologies. Sample sizes varied between 39 and 330. Some compounds (i.e, cyclohexane, P3, P5, P50, P71, diethyl ether, limonene, nonanal, VOC IK 1287) that can be indicative of MPM development in asbestos exposed population were identified with high diagnostic accuracy rates. E-nose studies reported breathprints being able to distinguish MPM from asbestos exposed individuals with high sensitivity and a negative predictive value. Small sample sizes and methodological diversities among studies limit the translation of results into clinical practice. More prospective studies with standardized methodologies should be conducted on larger populations.

The Use of Selected Ion Flow Tube-Mass Spectrometry Technology to Identify Breath Volatile Organic Compounds for the Detection of Head and Neck Squamous Cell Carcinoma: A Pilot Study

Background: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common form of cancer worldwide, with approximately 630,000 new cases diagnosed each year. The development of low-cost and non-invasive tools for the detection of HNSCC using volatile organic compounds (VOCs) in the breath could potentially improve patient care. The aim of this study was to investigate the feasibility of selected ion flow tube mass spectrometry (SIFT-MS) technology to identify breath VOCs for the detection of HNSCC. Materials and Methods: Breath samples were obtained from HNSCC patients (N = 23) and healthy volunteers (N = 21). Exhaled alveolar breath samples were collected into FlexFoil^® PLUS (SKC Limited, Dorset, UK) sampling bags from newly diagnosed, histologically confirmed, untreated patients with HNSCC and from non-cancer participants. Breath samples were analyzed by Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS) (Syft Technologies, Christchurch, New Zealand) using Selective Ion Mode (SIM) scans that probed for 91 specific VOCs that had been previously reported as breath biomarkers of HNSCC and other malignancies. Results: Of the 91 compounds analyzed, the median concentration of hydrogen cyanide (HCN) was significantly higher in the HNSCC group (2.5 ppb, 1.6–4.4) compared to the non-cancer group (1.1 ppb, 0.9–1.3; Benjamini–Hochberg adjusted p < 0.05). A receiver operating curve (ROC) analysis showed an area under the curve (AUC) of 0.801 (95% CI, 0.65952–0.94296), suggesting moderate accuracy of HCN in distinguishing HNSCC from non-cancer individuals. There were no statistically significant differences in the concentrations of the other compounds of interest that were analyzed. Conclusions: This pilot study demonstrated the feasibility of SIFT-MS technology to identify VOCs for the detection of HNSCC.

A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer

Lung cancer is the leading cause of cancer death around the world, and lung cancer screening remains challenging. This study aimed to develop a breath test for the detection of lung cancer using a chemical sensor array and a machine learning technique. We conducted a prospective study to enroll lung cancer cases and non-tumour controls between 2016 and 2018 and analysed alveolar air samples using carbon nanotube sensor arrays. A total of 117 cases and 199 controls were enrolled in the study of which 72 subjects were excluded due to having cancer at another site, benign lung tumours, metastatic lung cancer, carcinoma in situ, minimally invasive adenocarcinoma, received chemotherapy or other diseases. Subjects enrolled in 2016 and 2017 were used for the model derivation and internal validation. The model was externally validated in subjects recruited in 2018. The diagnostic accuracy was assessed using the pathological reports as the reference standard. In the external validation, the areas under the receiver operating characteristic curve (AUCs) were 0.91 (95% CI = 0.79–1.00) by linear discriminant analysis and 0.90 (95% CI = 0.80–0.99) by the supportive vector machine technique. The combination of the sensor array technique and machine learning can detect lung cancer with high accuracy.

Pilot Study: Detection of Gastric Cancer From Exhaled Air Analyzed With an Electronic Nose in Chinese Patients

The aim of this pilot study is to investigate the ability of an electronic nose (e-nose) to distinguish malignant gastric histology from healthy controls in exhaled breath. In a period of 3 weeks, all preoperative gastric carcinoma (GC) patients (n = 16) in the Beijing Oncology Hospital were asked to participate in the study. The control group (n = 28) consisted of family members screened by endoscopy and healthy volunteers. The e-nose consists of 3 sensors with which volatile organic compounds in the exhaled air react. Real-time analysis takes place within the e-nose, and binary data are exported and interpreted by an artificial neuronal network. This is a self-learning computational system. The inclusion rate of the study was 100%. Baseline characteristics differed significantly only for age: the average age of the patient group was 57 years and that of the healthy control group 37 years (P value = .000). Weight loss was the only significant different symptom (P value = .040). A total of 16 patients and 28 controls were included; 13 proved to be true positive and 20 proved to be true negative. The receiver operating characteristic curve showed a sensitivity of 81% and a specificity of 71%, with an accuracy of 75%. These results give a positive predictive value of 62% and a negative predictive value of 87%. This pilot study shows that the e-nose has the capability of diagnosing GC based on exhaled air, with promising predictive values for a screening purpose.

Analysis of volatile organic compounds: an innovative approach to heart failure characterization in older patients

BACKGROUND

Analysis of exhaled volatile organic compounds (VOCs) may be applied for diagnostic purposes in some chronic diseases, but there are no data on their role for discriminating people with congestive heart failure (CHF), particularly in older patients where natriuretic peptides have lower accuracy. We evaluated whether VOCs analysis can discriminate patients with or without CHF, stratify CHF severity and predict the response to therapy of decompensated CHF.

METHODS AND RESULTS

We recruited 89 subjects admitted to an acute care ward with acutely decompensated CHF, 117 healthy controls and 103 chronic obstructive pulmonary disease (COPD) controls. CHF patients performed echocardiography. VOCs were collected using the Pneumopipe^® and analyzed with the BIONOTE electronic nose. Partial least square analysis was used to evaluate the discriminative capacity of VOCs. Accuracy in discrimination of CHF versus healthy and COPD controls was 81% and 69%, respectively; accuracy did not decrease in a sensitivity analysis excluding subjects younger than 65 and older than 80 years. In CHF patients VOCs pattern could predict with fair precision ejection fraction and systolic pulmonary arterial pressure, but not changes in weight due to therapy.

CONCLUSIONS

VOCs pattern is able to discriminate older CHF patients from healthy people and COPD patients and correlates with cardiac function markers.

Electronic Nose Technology in Respiratory Diseases

Electronic noses (e-noses) are based on arrays of different sensor types that respond to specific features of an odorant molecule, mostly volatile organic compounds (VOCs). Differently from gas chromatography and mass spectrometry, e-noses can distinguish VOCs spectrum by pattern recognition. E-nose technology has successfully been used in commercial applications, including military, environmental, and food industry. Human-exhaled breath contains a mixture of over 3000 VOCs, which offers the postulate that e-nose technology can have medical applications. Based on the above hypothesis, an increasing number of studies have shown that breath profiling by e-nose could play a role in the diagnosis and/or screening of various respiratory and systemic diseases. The aim of the present study was to review the principal literature on the application of e-nose technology in respiratory diseases.